Improvement in Arctic sea ice data assimilation using the
randomized-dormant ensemble Kalman filter
Abstract
To improve the sea ice initial condition in the Arctic, we assimilate
the satellite-derived sea ice concentrations within the Data
Assimilation Research Testbed (DART) system, based on the ensemble
Kalman filter (EnKF), coupled with the sea ice model in the Community
Earth System Model (CESM). The EnKF-based assimilation results show that
the Arctic sea ice initial condition is significantly improved by
assimilating the satellite-derived sea ice concentration data. However,
during the Arctic sea ice freezing season, the assimilation impact tends
to be degraded due to the reduction of the ensemble spread within the
EnKF-based assimilation system. To counteract the ensemble spread
reduction, we apply the randomized-dormant ensemble Kalman filter
(RD-EnKF) method in which the model backgrounds are more perturbed by
leaving the dormant ensemble members out of the total ensemble members
from the analysis update, inflating the ensemble spread. Compared with
the assimilation results using the EnKF method, the additional analysis
benefits are obtained due to the increment of the ensemble spread
derived by applying the RD-EnKF method, in particular, during the Arctic
sea ice freezing season.